Autonomous Vehicle RL Decisions With Uncertainty-Based Fallback
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Solution Overview
Problem
Conventional decision-making methods for autonomous vehicles, particularly in tactical scenarios, struggle to handle uncertainty and adapt to diverse driving environments, often resulting in unsafe decisions when faced with unforeseen situations, and require extensive real-world testing to ensure reliability.
Innovation Solution
Implementing a reinforcement learning (RL) agent that estimates uncertainty through variability measures of state-action value functions, allowing for safe decision-making by executing tentative decisions only when uncertainty is below a predefined threshold and providing a safety-oriented fallback option.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning agents are trained to output decisions in diverse driving situations, then the adaptability of the decision-making system improves, but the reliability of decisions in uncertain or unseen situations deteriorates because the agents cannot indicate uncertainty
Solution Approach 1:
The decision-making system is segmented into multiple independent RL agents, each trained with different initial values. Each agent outputs a tentative decision independently, and the system evaluates the variability among these decisions to assess uncertainty. This segmentation allows the system to maintain adaptability while introducing a mechanism to detect unreliable decisions through variability measurement.
Solution Approach 2:
An uncertainty estimation module acts as an intermediary between the RL agents and the final decision execution. This module computes the variability measure of tentative decisions from multiple agents and uses it to determine whether to execute a decision or trigger a fallback mechanism. The intermediary preserves the adaptability of RL agents while adding a layer of reliability assessment.
2Reliability
If comprehensive real-world testing is conducted to ensure agent reliability, then the reliability of the decision-making system improves, but the time and resources required for development increase significantly
Solution Approach 1:
The system performs preliminary uncertainty assessment by computing variability measures during the decision-making process itself, rather than relying on extensive prior real-world testing. By training multiple RL agents with different initial values and using their variability as an uncertainty indicator, the system proactively identifies potentially unreliable decisions before execution, reducing the need for exhaustive testing and refinement iterations.
3Adaptability or versatility
If the RL agent outputs decisions for all possible actions in every state, then the completeness of decision coverage improves, but the complexity of evaluating and selecting the best action increases
Solution Approach 1:
The system uses multiple RL agents to generate tentative decisions, which may be more than strictly necessary (excessive action). However, the uncertainty estimation mechanism filters out unreliable decisions by measuring variability, effectively reducing the complexity of selecting the best action. This approach maintains comprehensive decision coverage while managing complexity through variability-based filtering.
Data Source
AI summary
A method of controlling an autonomous vehicle using a reinforcement learning, RL, agent. The method includes a plurality of training sessions, in which the RL agent interacts with an environment including the autonomous vehicle, each training session having a different initial value and yielding a state-action value function Qk(s, a) dependent on state and action; decision-making, in which the RL agent outputs at least one tentative decision relating to control of the autonomous vehicle; uncertainty estimation on the basis of a variability measure for the plurality of state-action value functions evaluated for a state-action pair corresponding to each of the tentative decisions; and vehicle control, wherein the at least one tentative decision is executed in dependence of the estimated uncertainty.